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    • Architecture to Distribute Deep Learning Models on Containers and Virtual Machines for Industry 4.0Peer reviewedClosed access 

      30 décembre 2023, LERAT, Jean-Sébastien; Mahmoudi, Sidi Ahmed, HE en Hainaut
      Acte de conférence ou de colloque
      eep learning (DL) is increasingly used in industry, especially in industry 4.0. Thanks to DL, it possible to better prevent breakdowns and manufacturing defects. DL models are becoming more and more complex and efficient, requiring significant compute resources and compute time. The use of Graphic Processing Units (GPUs) makes it possible to speed up processing but at a higher cost. An alternative ...

    • Distributed Deep Learning: From Single-Node to Multi-Node ArchitecturePeer reviewedClosed access 

      2022, LERAT, Jean-Sébastien; Mahmoudi, Sidi Ahmed; Mahmoudi, Saïd, HE en Hainaut
      Article scientifique
      During the last years, deep learning (DL) models have been used in several applications with large datasets and complex models. These applications require methods to train models faster, such as distributed deep learning (DDL). This paper proposes an empirical approach aiming to measure the speedup of DDL achieved by using different parallelism strategies on the nodes. Local parallelism is considered ...

    • Single node deep learning frameworks: Comparative study and CPU/GPU performance analysisPeer reviewedClosed access 

      2021, LERAT, Jean-Sébastien; Mahmoudi, Sidi Ahmed; Mahmoudi, Saïd, HE en Hainaut
      Article scientifique
      Deep learning presents an efficient set of methods that allow learning from massive volumes of data using complex deep neural networks. To facilitate the design and implementation of algorithms, deep learning frameworks provide a high-level programming interface. Based on these frameworks, new models, and applications are able to make better and better predictions. One type of deep learning application ...